



'''simple docstring'''

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)

class 							a__( unittest.TestCase     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=7					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=99					,   _UpperCAmelCase=32					,   _UpperCAmelCase=5					,   _UpperCAmelCase=4					,   _UpperCAmelCase=37					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=512					,   _UpperCAmelCase=16					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=4					,   )							->							List[Any]:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =seq_length
					snake_case__	     =is_training
					snake_case__	     =use_attention_mask
					snake_case__	     =use_token_type_ids
					snake_case__	     =use_labels
					snake_case__	     =vocab_size
					snake_case__	     =hidden_size
					snake_case__	     =num_hidden_layers
					snake_case__	     =num_attention_heads
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =max_position_embeddings
					snake_case__	     =type_vocab_size
					snake_case__	     =type_sequence_label_size
					snake_case__	     =initializer_range
					snake_case__	     =num_choices






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size						)

					snake_case__	     =None
					if self.use_attention_mask:
							snake_case__	     =random_attention_mask([self.batch_size, self.seq_length]						)

					snake_case__	     =None
					if self.use_token_type_ids:
							snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size						)

					snake_case__	     =RobertaPreLayerNormConfig(
					    vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_UpperCAmelCase					,   initializer_range=self.initializer_range					,   )

					return config, input_ids, token_type_ids, attention_mask






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.prepare_config_and_inputs()
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =config_and_inputs
					snake_case__	     ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
					return config, inputs_dict






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.prepare_config_and_inputs()
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =config_and_inputs

					snake_case__	     =True
					snake_case__	     =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2						)

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: List[Any]									=      True

			a_	: str									=      (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =FlaxRobertaPreLayerNormModelTester(self						)






			@slow
			def 						_lowercase  (     self						)							->							Any:
					for model_class_name in self.all_model_classes:
							snake_case__	     =model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=_UpperCAmelCase						)
							snake_case__	     =model(np.ones((1, 1)						)						)
							self.assertIsNotNone(_UpperCAmelCase						)





@require_flax
class 							a__( unittest.TestCase     ):






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=_UpperCAmelCase						)

					snake_case__	     =np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,   dtype=jnp.intaa						)
					snake_case__	     =model(_UpperCAmelCase						)[0]
					snake_case__	     =[1, 11, 5_0265]
					self.assertEqual(list(output.shape						)					,   _UpperCAmelCase						)
					# compare the actual values for a slice.
					snake_case__	     =np.array(
					    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]					,   dtype=np.floataa						)
					self.assertTrue(np.allclose(output[:, :3, :3]					,   _UpperCAmelCase					,   atol=1E-4						)						)






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=_UpperCAmelCase						)

					snake_case__	     =np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,   dtype=jnp.intaa						)
					snake_case__	     =model(_UpperCAmelCase						)[0]
					# compare the actual values for a slice.
					snake_case__	     =np.array(
					    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]					,   dtype=np.floataa						)
					self.assertTrue(np.allclose(output[:, :3, :3]					,   _UpperCAmelCase					,   atol=1E-4						)						)









'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     :						int	=  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: List[Any]									=      GPTSwaTokenizer
			a_	: int									=      False
			a_	: List[Any]									=      True
			a_	: Any									=      False






			def 						_lowercase  (     self						)							->							str:
					super().setUp()

					# We have a SentencePiece fixture for testing
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase					,   eos_token='<unk>'					,   bos_token='<unk>'					,   pad_token='<unk>'						)

					tokenizer.save_pretrained(self.tmpdirname						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     ='This is a test'
					snake_case__	     ='This is a test'
					return input_text, output_text






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ='<s>'
					snake_case__	     =1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =list(self.get_tokenizer().get_vocab().keys()						)

					self.assertEqual(vocab_keys[0]					,   '<unk>'						)
					self.assertEqual(vocab_keys[1]					,   '<s>'						)
					self.assertEqual(vocab_keys[-1]					,   'j'						)
					self.assertEqual(len(_UpperCAmelCase						)					,   2000						)






			def 						_lowercase  (     self						)							->							List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size					,   2000						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)

					snake_case__	     =tokenizer.tokenize('This is a test'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁This', '▁is', '▁a', '▁t', 'est']						)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [465, 287, 265, 631, 842]						)

					snake_case__	     =tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,   )
					# fmt: on

					snake_case__	     =tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
					self.assertListEqual(
					    _UpperCAmelCase					,   [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,   )

					snake_case__	     =tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
					# fmt: on






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)
					snake_case__	     =['This is a test', 'I was born in 92000, and this is falsé.']
					snake_case__	     =[
					    [465, 287, 265, 631, 842],
					    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
					]

					# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
					for text, expected_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					# Test that decode_fast returns the input text
					for text, token_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =[
					    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
					    'Hey there, how are you doing this fine day?',
					    'This is a text with a trailing spaces followed by a dot     .',
					    'Häj sväjs lillebrör! =)',
					    'Det är inget fel på Mr. Cool',
					]

					# fmt: off
					snake_case__	     ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
					# fmt: on
					self.tokenizer_integration_test_util(
					    expected_encoding=_UpperCAmelCase					,   model_name='AI-Sweden/gpt-sw3-126m'					,   sequences=_UpperCAmelCase					,   )




